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Bio-inspired Robotics

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Rapid Roboting

Part of the book series: Intelligent Systems, Control and Automation: Science and Engineering ((ISCA,volume 82))

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Abstract

The fields of artificial intelligence and bio-inspired robotics have proven to cross several other fields of expertise including Cognitive Neuroscience. Here, we review principles of interaction between a natural (or artificial) organism and the environment where it lives. Then we ask whether such structural coupling shapes the way it behaves. For instance, how the sensory processing of the external world controls actions, and finally, behavior? We remind the main sources of inspiration for bio-inspired robotics and relate them to currently active fields of research like Embodiment and Enaction. These latter concepts are illustrated by examples of recent researches on two main aspects: (i) bio-inspired algorithms processing sensory signals coming from the outer world and (ii) bio-inspired controllers based on human behavior and physiology. Finally, we include an example of a bio-inspired robot controller design based on the concepts here exposed.

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Notes

  1. 1.

    https://project.inria.fr/keops.

  2. 2.

    https://github.com/mjescobar/MODI.

  3. 3.

    Scenario with color cubes: raw visual input (https://youtu.be/dLpcimLrfkA); retina-based visual input (https://youtu.be/I9dhgVhbiVs). Scenario with textured cubes: raw visual input (https://youtu.be/xlW1cIa42ls); retina-based visual input (https://youtu.be/Y6eHLBWxPfg).

References

  1. Adams S, Arel I, Bach J, Coop R, Furlan R, Goertzel B, Hall JS, Samsonovich AV, Scheutz M, Schlesinger M, Shapiro SC, Sowa JF (2012) Mapping the landscape of human-level artificial general intelligence. AI Mag 33(1)

    Google Scholar 

  2. Alexandre F (2009) Cortical basis of communication: local computation, coordination, attention. Neural Netw 22(2):126–133

    Article  Google Scholar 

  3. Alexandre F, Guyot F, Haton JP, Burnod Y (1991) The cortical column: a new processing unit for multilayered networks. Neural Netw 4(1):15–25

    Article  Google Scholar 

  4. Arkin RC (1998) Behavior-based robotics. MIT Press, Cambridge

    Google Scholar 

  5. Attneave F (1954) Some informational aspects of visual perception. Psychol Rev 183–193

    Google Scholar 

  6. Aubert G, Kornprobst P (2006) Mathematics of image processing. In: Françoise JP, Naber G, Tsou S (eds) Encyclopedia of mathematical physics, vol 3. Elsevier, Oxford, pp 1–9. ftp://ftp-sop.inria.fr/odyssee/Publications/2006/aubert-kornprobst:06.pdf

  7. Barlow H (2001) Redundancy reduction revisited. Network 12(3):241–253

    Article  MathSciNet  Google Scholar 

  8. Barlow HB (1961) Possible principles underlying the transformation of sensory messages. Sens Commun 217–234

    Google Scholar 

  9. Baudot P (2006) Natural computation, much ado about nothing? PhD thesis, University Pierre et Marie Curie, Paris. http://tel.archives-ouvertes.fr/docs/00/20/37/12/PDF/These_piero_nature_is_the_code.pdf

  10. Beati T, Carrere M, Alexandre F (2013) Which reinforcing signals in autonomous systems? In: Third international symposium on biology of decision making, Paris, France. https://hal.inria.fr/hal-00826603

  11. Bobick A, Richards W (2006) Classifying objects from visual information. Technical report AIM-879, M.I.T. http://dspace.mit.edu/handle/1721.1/6443

  12. Bongard J, Lipson H (2005) Active coevolutionary learning of deterministic finite automata. J Mach Learn Res 6:1651–1678

    MathSciNet  MATH  Google Scholar 

  13. Bongard J, Zykov V, Lipson H (2006) Resilient machines through continuous self-modeling. Science 314(5802):1118–1121

    Article  Google Scholar 

  14. Brivanlou IH, Warland DK, Meister M (1998) Mechanisms of concerted firing among retinal ganglion cells. Neuron 20:527–529

    Article  Google Scholar 

  15. Brooks R (1986) A robust layered control system for a mobile robot. IEEE J Robot Autom 2(1):14–23

    Article  Google Scholar 

  16. Brooks RA (1991) Intelligence without representation. Artif Intell 47(1–3):139–159

    Article  Google Scholar 

  17. Brown C, Coombs D, Soong J (1993) Real-time smooth pursuit tracking. In: Blake A, Yuille A (eds) Active vision, chap VIII. The MIT Press, pp 123–136

    Google Scholar 

  18. Carrere M, Alexandre F (2015) A pavlovian model of the amygdala and its influence within the medial temporal lobe. Front Syst Neurosci 14. https://doi.org/10.3389/fnsys.2015.00041. https://hal.inria.fr/hal-01145790

  19. Carvajal C (2014) Dynamic interplay between standard and non-standard retinal pathways in the early thalamocortical visual system: a modeling study. PhD thesis, Université de Lorraine

    Google Scholar 

  20. Cessac B, Viéville T (2008) On dynamics of integrate-and-fire neural networks with adaptive conductances. Front Neurosci 2(2)

    Google Scholar 

  21. Comon P, Jutten C, Herault J (1991) Blind separation of sources, part II: problems statement. Signal Process 24:11–20. http://portal.acm.org/citation.cfm?id=119708

  22. Craig A (2009) How do you feel - now? The anterior insula and human awareness. Nat Rev Neurosci 10:59–70

    Article  Google Scholar 

  23. Dacey D (1999) Primate retina: cell types, circuits and color opponency. Prog Retin Eye Res 18(6):737–763

    Article  Google Scholar 

  24. Damasio A, Carvalho GB (2013) The nature of feelings: evolutionary and neurobiological origins. Nat Rev Neurosci 14(2):143–152

    Article  Google Scholar 

  25. Davis DN (2002) Computational architectures for intelligence and motivation. In: Proceedings of the 2002 IEEE international symposium on intelligent control. IEEE, pp 520–525

    Google Scholar 

  26. Dayan P, Abbott LF (2005) Theoretical neuroscience: computational and mathematical modeling of neural systems. The MIT Press, Cambridge

    Google Scholar 

  27. Denoyelle N, Pouget F, Viéville T, Alexandre F (2014) VirtualEnaction: a platform for systemic neuroscience simulation. In: International congress on neurotechnology, electronics and informatics, Rome, Italy. https://hal.inria.fr/hal-01063054

  28. DeVries S (1999) Correlated firing in rabbit retinal ganglion cells. J Neurophysiol 81(2):908–920

    Article  Google Scholar 

  29. Escobar MJ, Kornprobst P (2012) Action recognition via bio-inspired features: the richness of center-surround interactions. Comput Vis Image Underst 116:593–605

    Article  Google Scholar 

  30. Escobar MJ, Masson GS, Vieville T, Kornprobst P (2009) Action recognition using a bio-inspired feedforward spiking network. Int J Comput Vision 82(3):284

    Article  Google Scholar 

  31. Farabet C, Couprie C, Najman L, LeCun Y (2013) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35(8):1915–1929. http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.231

  32. Faugeras O, Luong Q, Papadopoulo T (2001) The geometry of multiple images. MIT Press, Cambridge

    Google Scholar 

  33. Field G, Chichilnisky E (2007) Information processing in the primate retina: circuitry and coding. Annu Rev Neurosci 30:1–30

    Article  Google Scholar 

  34. Floreano D, Suzuki M (2006) Active vision and neural development in animals and robots. In: Proceedings of the seventh international conference on cognitive modeling, pp 10–11. http://iccm2006.units.it/

  35. Friston K, Rigoli F, Ognibene D, Mathys C, Fitzgerald T, Pezzulo G (2015) Active inference and epistemic value. Cogn Neurosci 1–28. http://view.ncbi.nlm.nih.gov/pubmed/25689102

  36. Friston K, Schwartenbeck P, Fitzgerald T, Moutoussis M, Behrens T, Dolan RJ (2013) The anatomy of choice: active inference and agency. Front Human Neurosci 7. https://doi.org/10.3389/fnhum.2013.00598. http://dx.doi.org/10.3389/fnhum.2013.00598

  37. Geisler W (2008) Visual perception and the statistical properties of natural scenes. Annu Rev Psychol 59:167–192

    Article  Google Scholar 

  38. Giese M, Poggio T (2003) Neural mechanisms for the recognition of biological movements and actions. Nat Rev Neurosci 4:179–192

    Article  Google Scholar 

  39. Girard B, Cuzin V, Guillot A, Gurney K, Prescott T (2003) A basal-ganglia inspired model of action selection evaluated in a robotic survival task. J Integr Neurosci 2(2):179–200

    Article  Google Scholar 

  40. Gollisch T, Meister M (2008) Rapid neural coding in the retina with relative spike latencies. Science 319:1108–1111. https://doi.org/10.1126/science.1149639

    Article  Google Scholar 

  41. Gurney K, Prescott T, Wickens J, Redgrave P (2004) Computational models of the basal ganglia: from robots to membranes. Trends Neurosci 27(8):453–459

    Article  Google Scholar 

  42. ter Haar Romeny BM (2003) Front-end vision and multi-scale image analysis - multi-scale computer vision theory and applications, written in mathematics. Comput Imaging Vis vol 27. Springer, Berlin

    Google Scholar 

  43. Harnard S (1990) The symbol grounding problem. Physica D 42:335–346

    Article  Google Scholar 

  44. Henderson TC (1992) Object identification in context: the visual processing of natural scenes. Can J Psychol: Special Issue Object Scene Process 46:319–342

    Article  Google Scholar 

  45. Hubel D, Wiesel T (1962) Receptive fields, binocular interaction and functional architecture in the cat visual cortex. J Physiol 160:106–154

    Article  Google Scholar 

  46. Hyvärinen (2009) Natural image statistics. Springer, Berlin. http://www.cs.helsinki.fi/u/ahyvarin/papers/viscor.shtml

  47. Iocchi L, Nardi D, Salerno M (2001) Reactivity and deliberation: a survey on multi-robot systems. In: Balancing reactivity and social deliberation in multi-agent systems. Springer, Berlin, pp 9–32

    Google Scholar 

  48. Jhuang H, Serre T, Wolf L, Poggio T (2007) A biologically inspired system for action recognition. In: ICCV, pp 1–8

    Google Scholar 

  49. Kaplan F, Oudeyer PY (2008) Intrinsically motivated machines. In: Lungarella M, Iida F, Bongard J, Pfeifer R (eds) 50 Years of AI, p. n/a. Lungarella M, Iida F, Bongard J, Pfeifer R. https://hal.inria.fr/inria-00420223

  50. Karl F (2012) A free energy principle for biological systems. Entropy 14(11):2100–2121. https://doi.org/10.3390/e14112100. http://dx.doi.org/10.3390/e14112100

  51. Kassab R, Alexandre F (2009) Incremental data-driven learning of a novelty detection model for one-class classification with application to high-dimensional noisy data. Mach Learn 74(2):191–234

    Article  MATH  Google Scholar 

  52. Knill DC, Richards W (eds) (1996) Perception as bayesian inference. Cambridge University Press, New York

    MATH  Google Scholar 

  53. Kornprobst P, Vieville T, Chemla S, Rochel O (2006) Modeling cortical maps with feed-backs. In: 29th European conference on visual perception, p 53

    Google Scholar 

  54. Krichmar JL (2013) A neurorobotic platform to test the influence of neuromodulatory signaling on anxious and curious behavior. Front Neurorobot 7

    Google Scholar 

  55. Krichmar JL, Wagatsuma H (2011) Neuromorphic and brain-based robots. Cambridge University Press, Cambridge

    Google Scholar 

  56. Kurzweil R (2012) How to create a mind: the secret of human thought revealed. Penguin, London

    Google Scholar 

  57. LeDoux J (2007) The amygdala. Curr Biol 17(20):R868–R874

    Article  Google Scholar 

  58. Marr D (1982) Vision: A computational investigation into the human representation and processing of visual information. W.H. Freeman, New York

    Google Scholar 

  59. Masland R (2001) The fundamental plan of the retina. Nature Neurosci 4(9)

    Google Scholar 

  60. Masland R (2001) Neuronal diversity in the retina. Curr Opin Neurobiol 11(4):431–436

    Article  Google Scholar 

  61. Masland RH, Martin PR (2007) The unsolved mystery of vision. Current Biol 17(15):R577–R582. https://doi.org/10.1016/j.cub.2007.05.040. http://dx.doi.org/10.1016/j.cub.2007.05.040

  62. Mastronarde D (1983) Correlated firing of cat retinal ganglion cells. I. Spontaneously active inputs to X-and Y-cells. J Neurophysiol 49(2):303–324

    Google Scholar 

  63. Maunsell J, Newsome W (1987) Visual processing in monkey extrastriate cortex. Ann Rev Neurosci 10:363–401

    Article  Google Scholar 

  64. Medioni G, Kang S (2004) Emerging topics in computer vision. Prentice Hall, Hoboken

    Google Scholar 

  65. Meister M, Pine J, Baylor DA (1994) Multi-neuronal signals from the retina: acquisition and analysis. J Neurosci Methods 51(1):95–106. http://view.ncbi.nlm.nih.gov/pubmed/8189755

  66. Meyer JA, Guillot A, Girard B, Khamassi M, Pirim P, Berthoz A (2005) The psikharpax project: towards building an artificial rat. Robot Auton Syst 211–223

    Google Scholar 

  67. Milner AD, Goodale MA (2008) Two visual systems re-viewed. Neuropsychologia 46:774–785

    Article  Google Scholar 

  68. Minsky M (1988) Society of mind. A touchstone book. Simon & Schuster, New York

    Google Scholar 

  69. Minsky M (2007) The emotion machine: commonsense thinking, artificial intelligence, and the future of the human mind. Simon & Schuster, New York

    Google Scholar 

  70. Moulin-Frier C, Nguyen SM, Oudeyer PY (2013) Self-organization of early vocal development in infants and machines: the role of intrinsic motivation. Front Psychol 4(1006). https://doi.org/10.3389/fpsyg.2013.01006. https://hal.inria.fr/hal-00927940

  71. Neuenschwander S, Singer W (1996) Long-range synchronization of oscillatory light responses in the cat retina and lateral geniculate nucleus. Nature 379(6567):728–732

    Article  Google Scholar 

  72. Nirenberg S, Latham P (1998) Population coding in the retina. Curr Opin Neurobiol 8:488–493

    Article  Google Scholar 

  73. O’Regan K, Noe A (2001) A sensorimotor account of vision and visual consciousness. Behav Brain Sci 24:939–1031

    Article  Google Scholar 

  74. O’Reilly R, Munakata Y, Frank MJ, Hazy TE (2014) Goal-driven cognition in the brain: a computational framework (2014). arXiv:1404.7591

  75. Pezzulo G, Verschure PFMJ, Balkenius C, Pennartz CMA (2014) The principles of goal-directed decision-making: from neural mechanisms to computation and robotics. Philos Trans R Soc Lond B: Biolog Sci 369(1655):20130,470+. https://doi.org/10.1098/rstb.2013.0470

  76. Pfeifer R, Bongard J, Grand S (2007) How the body shapes the way we think: a new view of intelligence, Bradford Books. MIT Press, Cambridge

    Google Scholar 

  77. Philipona D, O’Regan K, Nadal JP, Coenen OM (2004) Perception of the structure of the physical world using unknown sensors and effectors. In: Thrun S, Saul L, Scholkopf B (eds) Advances in neural information processing systems 16. MIT Press, Cambridge

    Google Scholar 

  78. Prescott TJ, Fernando, Gurney K, Humphries MD, Redgrave P (2006) A robot model of the basal ganglia: behavior and intrinsic processing. Neural Netw 19(1):31–61

    Google Scholar 

  79. Rao R, Sejnowski TJ (1991) Predictive sequence learning in recurrent neocortical circuits. Advances in neural information and processing systems, vol 12. MIT Press, Cambridge

    Google Scholar 

  80. Rohmer E, Singh SP, Freese M (2013) V-rep: a versatile and scalable robot simulation framework. In: 2013 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 1321–1326

    Google Scholar 

  81. Rubilar F, Escobar MJ, Arredondo T (2014) Bio-inspired architecture for a reactive-deliberative robot controller. In: 2014 international joint conference on neural networks (IJCNN). IEEE, pp 2027–2035

    Google Scholar 

  82. Rullen RV, Thorpe S (2001) Rate coding versus temporal order coding: What the retina ganglion cells tell the visual cortex. Neural Comput 13(6):1255–1283

    Article  MATH  Google Scholar 

  83. Russell S, Norvig P (2003) Artificial intelligence - a modern approach. Prentice-Hall, Upper Saddle River

    MATH  Google Scholar 

  84. Saez S (2013) Diseño y construcción de plataforma para estudio de enjambres de robots. PhD thesis, Professional Engineer’s thesis, Department of Electronic Engineering. Universidad Técnica Federico Santa María

    Google Scholar 

  85. Samson C, Leborgne M, Espiau B (1991) Robot control. The task-function approach. In: Oxford Engineering Science Series, vol 22. Oxford University Press, Oxford

    Google Scholar 

  86. Schneidman E, Berry M, Segev R, Bialek W (2006) Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440(7087):1007–1012

    Article  Google Scholar 

  87. Schwartz G, Berry MJ (2008) Sophisticated temporal pattern recognition in retinal ganglion cells. J Neurophysiol 99(4):1787–1798. https://doi.org/10.1152/jn.01025.2007

    Article  Google Scholar 

  88. Schwartz G, Harris R, Shrom D, Berry MJ (2007) Detection and prediction of periodic patterns by the retina. Nature Neurosci 10(5):552–554. https://doi.org/10.1038/nn1887. http://dx.doi.org/10.1038/nn1887

  89. Schwartz G, Taylor S, Fisher C, Harris R, Berry MJ (2007) Synchronized firing among retinal ganglion cells signals motion reversal. Neuron 55(6):958–969. https://doi.org/10.1016/j.neuron.2007.07.042. http://dx.doi.org/10.1016/j.neuron.2007.07.042

  90. Searle JR (1980) Minds, brains, and programs. Behav Brain Sci 3:417–457

    Article  Google Scholar 

  91. Serre T (2006) Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines. PhD thesis, Massachusetts Institute of Technology, Cambridge, MA

    Google Scholar 

  92. Serre T, Wolf L, Poggio T (2005) Object recognition with features inspired by visual cortex. In: CVPR, pp 994–1000

    Google Scholar 

  93. Shannon C (1948) A mathematical theory of communication. Bell Syst Techn J 27:379–423, 623–656. http://cm.bell-labs.com/cm/ms/what/shannonday/ shannon1948.pdf

  94. Shlens J, Field G, Gauthier J, Grivich M, Petrusca D, Sher A, Litke A, Chichilnisky E (2006) The structure of multi-neuron firing patterns in primate retina. J Neurosci 26(32):8254

    Article  Google Scholar 

  95. Simoncelli E, Olshausen B (2001) Natural image statistics and neural representation. Annu Rev Neurosci 24(1):1193–1216

    Article  Google Scholar 

  96. Singh P, Minsky M (2003) An architecture for combining ways to think. In: International conference on integration of knowledge intensive multi-agent systems. IEEE, pp 669–674

    Google Scholar 

  97. Singh P, Minsky M (2005) An architecture for cognitive diversity. In: Visions of mind: architectures for cognition and affect. IGI Global, pp 312–331

    Google Scholar 

  98. Smith E, DeCoster J (2000) Dual-process models in social and cognitive psychol- ogy: conceptual integration and links to underlying memory systems. Pers Soc Psychol Rev 4(2):108–131

    Article  Google Scholar 

  99. Stanley K (2007) Compositional pattern producing networks: A novel abstraction of development. Genet Program Evolvable Mach 8(2):131–162

    Article  Google Scholar 

  100. Stanley K, Miikkulainen R (2002) Evolving neural networks through augmenting topologies. Evol Comput 10(2):99–127

    Article  Google Scholar 

  101. Strack F, Deutsch R (2002) Reflective and impulsive determinants of social behavior. Pers Soc Psychol Rev 8(3):220–247

    Article  Google Scholar 

  102. Suzuki M, Floreano D (2008) Enactive robot vision. Adapt Behav 16(2–3):122–128

    Article  Google Scholar 

  103. Taouali W, Goffart L, Alexandre F, Rougier NP (2015) A parsimonious computational model of visual target position encoding in the superior colliculus. Biol Cybern. In Press

    Google Scholar 

  104. Teftef E, Escobar MJ, Astudillo A, Carvajal C, Cessac B, Palacios A, Viéville T, Alexandre F (2013) Modeling non-standard retinal in/out function using computer vision variational methods. Research Report RR-8217, INRIA. https://hal.inria.fr/hal-00783091

  105. Thompson E, Palacios A, Varela F (1992) Ways of coloring: comparative color vision as a case study for cognitive science. Behav Brain Sci 15:1–26

    Article  Google Scholar 

  106. Todorov E (2004) Optimally principles in sensorimotor control. Nature Neurosci 7(9):907–915

    Article  Google Scholar 

  107. Torralba A, Oliva A (2003) Statistics of natural image categories. Netw: Comput Neural Syst 14:391–412. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.6355

  108. Van Essen DC, Gallant JL (1994) Neural mechanisms of form and motion processing in the primate visual system. Neuron 13:1–10

    Article  Google Scholar 

  109. VanRullen R, Thorpe SJ (2002) Surfing a spike wave down the ventral stream. Vision Res 42:2593–2615

    Article  Google Scholar 

  110. Varela F, Thompson E, Rosh E (2001) The embodied mind: cognitive science and human experience. The MIT Press, Cambridge

    Google Scholar 

  111. Varela FJ (1979) Principles of biological autonomy. The North Holland series in general systems research. North Holland, New York

    Google Scholar 

  112. Vieville T (1997) A few steps towards 3D active vision. Springer, Berlin

    Google Scholar 

  113. Vieville T, Crahay S (2004) Using an hebbian learning rule for multi-class svm classifiers. J Comput Neurosci 17(3):271–287

    Article  Google Scholar 

  114. Wang Y, Li S, Chen Q, Hu W (2007) Biology inspired robot behavior selection mechanism: Using genetic algorithm. In: Li K, Fei M, Irwin G, Ma S (eds) Bio-inspired computational intelligence and applications, vol 4688. Lecture notes in computer science. Springer, Berlin, pp 777–786

    Google Scholar 

  115. Wassle H (2004) Parallel processing in the mammalian retina. Nat Rev Neurosci 5(10):747–57

    Article  Google Scholar 

  116. Wohrer A, Kornprobst P (2009) Virtual retina : a biological retina model and simulator, with contrast gain control. J Comput Neurosci 26(2):219

    Article  MathSciNet  Google Scholar 

  117. Zagal J, Lipson H (2011) Towards self-reflecting machines: two-minds in one robot. In: Kampis G, Karsai I, Szathmáry E (eds) Advances in artificial life, vol 5777. Darwin meets von neumann, Lecture notes in computer science. Springer, Berlin, pp 156–164

    Google Scholar 

  118. Zagal JC, Lipson H (2009) Self-reflection in evolutionary robotics: resilient adaptation with a minimum of physical exploration. In: Proceedings of the 11th annual conference companion on genetic and evolutionary computation conference: Late Breaking Papers. ACM, pp 2179–2188

    Google Scholar 

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Acknowledgements

This work was partially supported by ANR-CONICYT KEOPS (ANR-47); ECOS-CONICYT C13E06; FONDECYT Nro. 1140403, Nro. 1150638; AFOSR Grant Nro. FA9550-19-1-0002; UTFSM DGIP-Grant 231358; Millennium Institute ICM-P09-022-F; Basal Project FB0008. We would also like to thank Patricio Cerda for the simulations performed using MODI and V-REP platform described in Sect. 4.

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Authors and Affiliations

  1. Departamento de Electrónica, Universidad Técnica Federico Santa María, Avda. España 1680, 2390123, Valparaíso, Chile

    María-José Escobar

  2. Mnemosyne Research Team, Inria Bordeaux Sud-Ouest, Talence, France

    Frédéric Alexandre

  3. LaBRI, UMR 5800, CNRS, Bordeaux INP, Université de Bordeaux, Talence, France

    Frédéric Alexandre

  4. CNRS, Institut des Maladies Neurodégénératives, UMR 5293, Université de Bordeaux, Bordeaux, France

    Frédéric Alexandre & Thierry Viéville

  5. Mnemosyne Research Team, Inria Bordeaux Sud-Ouest, Talence, France

    Thierry Viéville

  6. Laboratoire LINE, Université Côte d’Azur, Nice, France

    Thierry Viéville

  7. Facultad de Ciencias, Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, 2360102, Valparaíso, Chile

    Adrian Palacios

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  1. María-José Escobar
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Correspondence to María-José Escobar .

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Editors and Affiliations

  1. Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Fernando Auat

  2. Department of Engineering Design, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Pablo Prieto

  3. Research Center E. Piaggio Faculty of Engineering, University of Pisa, Pisa, Italy

    Gualtiero Fantoni

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Escobar, MJ., Alexandre, F., Viéville, T., Palacios, A. (2022). Bio-inspired Robotics. In: Auat, F., Prieto, P., Fantoni, G. (eds) Rapid Roboting. Intelligent Systems, Control and Automation: Science and Engineering, vol 82. Springer, Cham. https://doi.org/10.1007/978-3-319-40003-7_8

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